shrinkDSM: Efficient Bayesian Inference for Dynamic Survival Models with Shrinkage

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of dynamic survival models with shrinkage priors. Details on the algorithms used are provided in Wagner (2011) <doi:10.1007/s11222-009-9164-5>, Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and Cadonna et al. (2020) <doi:10.3390/econometrics8020020>.

Version: 0.2.0
Depends: R (≥ 3.3.0)
Imports: Rcpp, stochvol (≥ 3.0.3), coda, utils, shrinkTVP (≥ 2.0.2)
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, stochvol, shrinkTVP
Suggests: testthat (≥ 3.0.0)
Published: 2022-11-15
DOI: 10.32614/CRAN.package.shrinkDSM
Author: Daniel Winkler [aut, cre], Peter Knaus ORCID iD [aut]
Maintainer: Daniel Winkler <daniel.winkler at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: shrinkDSM results


Reference manual: shrinkDSM.pdf


Package source: shrinkDSM_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): shrinkDSM_0.2.0.tgz, r-oldrel (arm64): shrinkDSM_0.2.0.tgz, r-release (x86_64): shrinkDSM_0.2.0.tgz, r-oldrel (x86_64): shrinkDSM_0.2.0.tgz
Old sources: shrinkDSM archive


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